## Source: local data table [475,719 x 102]
## Call:   `_DT1`[!`_DT2`, on = .(word)]
## 
##   FACILITY_CITY ACTIVI…¹ OWNER…² OWNER…³ FACIL…⁴ RECOR…⁵ PROGR…⁶ PROGR…⁷ PROGR…⁸
##   <chr>         <chr>    <chr>   <chr>   <chr>   <chr>   <chr>   <chr>     <int>
## 1 ALHAMBRA      2021/10… OW0269… SKATE … FA0280… PR0235… SKATES… ACTIVE     1634
## 2 ALHAMBRA      2018/05… OW0031… SANCHE… FA0006… PR0037… BUN N … ACTIVE     1638
## 3 ALHAMBRA      2018/05… OW0031… SANCHE… FA0006… PR0037… BUN N … ACTIVE     1638
## 4 ALHAMBRA      2021/07… OW0033… STARBU… FA0048… PR0005… STARBU… ACTIVE     1633
## 5 ALHAMBRA      2021/07… OW0033… STARBU… FA0048… PR0005… STARBU… ACTIVE     1633
## 6 ALHAMBRA      2019/05… OW0185… SAN TU… FA0179… PR0173… RICK'S… ACTIVE     1638
## # … with 475,713 more rows, 93 more variables: PE_DESCRIPTION <chr>,
## #   FACILITY_ADDRESS <chr>, FACILITY_STATE <chr>, FACILITY_ZIP <int>,
## #   SERVICE_CODE <int>, SERVICE_DESCRIPTION <chr>, SCORE <int>,
## #   SERIAL_NUMBER <chr>, EMPLOYEE_ID <chr>, ObjectId.x <int>, Pop_Tot <int>,
## #   Prop_18y <dbl>, Prop_64y <dbl>, Prop_65y_ <dbl>, Prop_Blk <dbl>,
## #   Prop_Lat <dbl>, Prop_Whi <dbl>, Prop_Asi <dbl>, Prop_Ami <dbl>,
## #   Prop_NHO <dbl>, Prop_FPL1 <dbl>, Prop_FPL2 <dbl>, Prop_forb <dbl>, …
## 
## # Use as.data.table()/as.data.frame()/as_tibble() to access results

Showcasing plots

Restaurant Inspection Score vs Diabetes

Restaurant Inspection Score vs Obesity

scatter2

Restaurant Inspection Score vs Depression

scatter3

Map of chain restaurants with heat map of proportion with diabetes

DMmap

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